The United Nations' Sustainable Development Goals (SDGs) explicitly acknowledge inclusive and equitable quality education as the primary goal of any global initiatives for early childhood development for children under 5 years with developmental delays and disabilities. Primary education provides the foundation for lifelong learning, vocational attainment, and economically independent living. Globally, the majority (over 90%) of children with developmental disabilities reside in low- and middle-income countries (LMICs). These children are significantly less likely to have foundational reading and numeracy skills, more likely to have never attended school and more likely to be out of primary school, compared to children without disabilities. Concerted and well-coordinated efforts to prepare these children in early childhood for inclusive education constitute a moral and ethical priority for all countries. This paper sets out to examine the concept and dimensions of school readiness for children under 5 years from an extensive narrative review of the literature. It identifies the barriers and challenges for school readiness for children with disabilities and the limitations of the available tools for evaluating school readiness. It concludes by emphasizing the critical role of inter-disciplinary engagement among pediatric caregivers in promoting school readiness in partnership with the families and community where the children reside. Overall, the paper highlights the need for appropriate policy initiatives at the global and national levels to promote school readiness specifically for children under 5 years with developmental disabilities in LMICs, if the aspirational goal of inclusive education by 2030 under the SDGs is to be realized.

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Polycystic Ovary Syndrome (PCOS) is one of the profound causes of infertility in women. Early detection, and treatment is essential in improving the prognosis in patients. The current conditions of fertility in India are skeptical, wherein women are at higher risk. PCOS is one of the major causes of infertility and scales upto 20% of women population in India. This requires a timely and accurate diagnosis which can be accomplished by developing automated diagnosing models. Having noted that the data to be dealt with consists of both clinical and non-clinical inputs, the effective information alone needs to be considered for the diagnosis. This necessitates an intelligent selection of features before diagnosing. Thus, swarm intelligence (SI) for feature selection and machine learning for classification is considered to develop a robust and efficient diagnostic model to detect PCOS condition. Initially, optimal features are selected using statistical approaches namely, correlation and Chi Square test and exhaustive search procedure by recursive elimination. Further, the SI algorithms, Particle Swarm Optimization (PSO) and Flashing firefly (FF) are attempted to identify the optimal number and feasible combination of features. Random forest classifier has been used in the ML model for classification. A comparative analysis of the results is discussed and validated based on the parameters accuracy of training and testing, precision, recall, F1-score, and AUC-ROC. The results reveal that ML models with different feature selection algorithms give best performance for different feature dimensions and the model with PSO based feature selection gives the highest performance with minimum feature size. Also PSO based algorithm evades the problem of redundancy in the feature subset.

Polycystic Ovary Syndrome (PCOS) is one of the profound causes of infertility in women. Early detection, and treatment is essential in improving the prognosis in patients. The current conditions of fertility in India are skeptical, wherein women are at higher risk. PCOS is one of the major causes of infertility and scales upto 20% of women population in India. This requires a timely and accurate diagnosis which can be accomplished by developing automated diagnosing models. Having noted that the data to be dealt with consists of both clinical and non-clinical inputs, the effective information alone needs to be considered for the diagnosis. This necessitates an intelligent selection of features before diagnosing. Thus, swarm intelligence (SI) for feature selection and machine learning for classification is considered to develop a robust and efficient diagnostic model to detect PCOS condition. Initially, optimal features are selected using statistical approaches namely, correlation and Chi Square test and exhaustive search procedure by recursive elimination. Further, the SI algorithms, Particle Swarm Optimization (PSO) and Flashing firefly (FF) are attempted to identify the optimal number and feasible combination of features. Random forest classifier has been used in the ML model for classification. A comparative analysis of the results is discussed and validated based on the parameters accuracy of training and testing, precision, recall, F1-score, and AUC-ROC. The results reveal that ML models with different feature selection algorithms give best performance for different feature dimensions and the model with PSO based feature selection gives the highest performance with minimum feature size. Also PSO based algorithm evadesthe problem of redundancy in the feature subset.

No abstract